Hybrid Research Associate: Provenance & AI-Driven Workflows

Oak Ridge National Laboratory; Oak Ridge (TN); Hybrid; USD 120,000 - 160,000; Full time

Benefits offered by this job

Flexible hybrid/onsite schedule

On-site fitness facilities

Job summary

Oak Ridge National Laboratory (ORNL) seeks a senior contributor to design provenance and workflow systems for autonomous, AI-driven science. You will instrument distributed workflows, support cross-facility data and model artifact tracking, and translate scientific needs into scalable, auditable automation.

You will collaborate with researchers across DOE facilities, contribute to open-source software, and publish results in premier venues.

Qualifications

• PhD in CS/CE or MS with related experience.
• Experience designing or operating distributed systems or orchestration frameworks.
• Familiar with agentic workflows or AI agent frameworks.
• At least 1 year of Python; Bash and/or C/C++ experience.
• Experience with Linux HPC environments and job schedulers.

Responsibilities

• Develop provenance capture and data integration for scalable workflows.
• Support cross-facility integration, schemas, APIs and metadata standards.
• Assist platform integration and architectural trade-offs toward production.
• Design agentic workflows enabling autonomous, auditable experiments.
• Contribute to open-source software, CI/CD, testing and docs.
• Collaborate with universities, labs and international partners.
• Translate domain science needs into scalable workflow and data solutions.
• Publish research and present at conferences.

Skills

Python

Distributed systems

Workflow orchestration

Provenance models

AI agent frameworks

Linux

Communication

Education

PhD in Computer Science

MS in Computer Science

Tools

Flowcept

SLURM

CI/CD

Docker

Job description

Oak Ridge National Laboratory (ORNL) seeks a senior contributor to design provenance and workflow systems for autonomous, AI-driven science. You will instrument distributed workflows, support cross-facility data and model artifact tracking, and translate scientific needs into scalable, auditable automation.

You will collaborate with researchers across DOE facilities, contribute to open-source software, and publish results in premier venues.